CN109900298B - Vehicle positioning calibration method and system - Google Patents
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Abstract
The invention discloses a vehicle positioning calibration method and system, which are used in the field of automatic driving. The method provided by the invention comprises the following steps: acquiring the current position of a running vehicle, and acquiring laser radar point cloud data of the current position of the running vehicle; acquiring reference point cloud data closest to the current position of the vehicle according to the current position of the vehicle; according to the laser radar point cloud data of the current position of the vehicle and the reference point cloud data, calculating the transverse offset distance of the current position of the vehicle relative to a reference route through an ICP point cloud pairing algorithm; and calculating the actual position of the vehicle after calibration according to the transverse offset distance and the current position of the vehicle. According to the invention, the actual position of the vehicle can be accurately determined by comparing the reference point cloud data with the actually-collected point cloud data, and the influence of environmental factors on the positioning of the vehicle is reduced, so that the safety of automatic driving is ensured.
Description
Technical Field
The invention relates to the field of automatic driving, in particular to a vehicle positioning and calibrating method and system.
Background
In the running process of the automatic driving vehicle, the position of the vehicle needs to be acquired in real time, and the running track of the vehicle is adjusted according to the positioning of the vehicle so as to enable the vehicle to run along a target route. The position of the vehicle is typically obtained by high-precision combined inertial navigation, or directly by GPS positioning. In the running process of the vehicle, when the vehicle encounters a shelter from trees, tall buildings, tunnels, viaducts and the like, the positioning of the vehicle is disturbed, and accumulation is formed for a long time, so that the positioning accuracy of the vehicle is obviously reduced.
Disclosure of Invention
The embodiment of the invention provides a vehicle positioning calibration method and a system, which are used for calibrating the output vehicle position in real time and ensuring the accuracy of vehicle positioning.
In a first aspect of an embodiment of the present invention, there is provided a vehicle positioning calibration method, including:
acquiring the current position of a running vehicle, and acquiring laser radar point cloud data of the current position of the running vehicle;
acquiring reference point cloud data closest to the current position of the vehicle according to the current position of the vehicle, wherein the reference point cloud data are laser radar point cloud data acquired at intervals of a preset distance when the vehicle runs along a reference route;
according to the laser radar point cloud data of the current position of the vehicle and the reference point cloud data, calculating the transverse offset distance of the current position of the vehicle relative to a reference route through an ICP point cloud pairing algorithm;
and calculating the actual position of the vehicle after calibration according to the transverse offset distance and the current position of the vehicle.
In a second aspect of embodiments of the present invention, there is provided a vehicle positioning calibration system comprising:
and the acquisition module is used for: the method comprises the steps of acquiring the current position of a running vehicle and acquiring laser radar point cloud data of the current position of the running vehicle;
the acquisition module is used for: the method comprises the steps of acquiring reference point cloud data closest to the current position of a vehicle according to the current position of the vehicle, wherein the reference point cloud data are laser radar point cloud data acquired at intervals of a preset distance when the vehicle runs along a reference route;
a first calculation module: the method comprises the steps of calculating a transverse offset distance of a current position of the vehicle relative to a reference route through an ICP point cloud pairing algorithm according to laser radar point cloud data of the current position of the vehicle and the reference point cloud data;
a second calculation module: and the method is used for calculating the actual position of the vehicle after the vehicle is calibrated according to the transverse offset distance and the current position of the vehicle.
From the above technical solutions, the embodiment of the present invention has the following advantages:
according to the embodiment of the invention, the laser radar point cloud data of the positioning position and the reference position are compared, the transverse offset of the vehicle is obtained, the actual position of the vehicle is calculated according to the transverse offset distance, the influence of environmental factors on positioning can be reduced based on the position offset calibration of the point cloud data, so that the accuracy of the position of the automatic driving vehicle is ensured, and the positioning of the vehicle can be checked and corrected at any time according to the pre-acquired reference point cloud data aiming at various road conditions.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a vehicle positioning calibration method according to an embodiment of the present invention;
FIG. 2 is another schematic diagram of a vehicle positioning calibration method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a vehicle positioning calibration system according to an embodiment of the present invention;
Detailed Description
The embodiment of the invention provides a vehicle positioning calibration method and system, which are used for solving the problems of error and low accuracy of vehicle positioning.
In order to make the objects, features and advantages of the present invention more comprehensible, the technical solutions in the embodiments of the present invention are described in detail below with reference to the accompanying drawings, and it is apparent that the embodiments described below are only some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one:
referring to fig. 1, a flow chart of a vehicle positioning calibration method according to an embodiment of the invention includes:
s101, acquiring the current position of a running vehicle, and acquiring laser radar point cloud data of the current position of the running vehicle;
the current position is a GNSS (Global Navigation Satellite System) position of the vehicle obtained by a combined inertial navigation device of the running vehicle, and generally comprises longitude, latitude and course angle of the vehicle. The laser radar point cloud data refer to the surrounding environment of the vehicle at the current position at the current moment of scanning the current position of the vehicle through the multi-line laser radar, and generally comprises three-dimensional coordinate information of the current position and also can comprise reflection intensity and the like.
S102, acquiring reference point cloud data closest to the current position of the vehicle according to the current position of the vehicle, wherein the reference point cloud data are laser radar point cloud data acquired at intervals of a preset distance when the vehicle runs along a reference route;
when the automatic driving vehicle deviates from a reference route (or a target route), the positioning positions of the vehicle generally appear on two sides of the reference route, and according to the positioning positions, the point which corresponds to the nearest distance on the reference route is found, the reference route on which the point is positioned has reference point cloud data, and the reference route has a pair of laser radar point clouds at certain intervals.
The reference route is a pre-planned vehicle driving route, on the planned route, the vehicle is driven in advance according to the planned route, laser radar point cloud data are collected once every a distance, and a point cloud database of the planned route is formed. The reference route may be a center line of the lane. The distance between the two positions is generally smaller, for example, 1m or 2m, and the smaller the distance is, the higher the calculation accuracy of the final actual position is.
On the reference route, the point closest to the positioning position and the point cloud data acquisition point are different points on one route, and the point cloud data can be obtained by finding the point cloud data acquisition point closest to the positioning position.
Optionally, the vehicle runs to an initial position, and the vehicle collects a pair of laser radar point clouds at intervals according to the center line of the current lane, and records all the collected positions and the laser radar point cloud data corresponding to the collected positions.
Optionally, according to the current position of the vehicle, acquiring the position of a point of the current position of the vehicle, which is closest to a reference route, and the point being located on the reference route; and searching the reference point cloud data closest to the point.
S103, calculating the transverse offset distance of the current position of the vehicle relative to a reference route through an ICP point cloud pairing algorithm according to the laser radar point cloud data of the current position of the vehicle and the reference point cloud data;
the ICP (Iterative Closest Point) point cloud pairing algorithm is to combine point cloud data under different coordinates into the same coordinate system, and essentially, based on a least square method, the corresponding relation point pairs are repeatedly selected, and the optimal rigid body transformation is calculated until the convergence accuracy requirement of correct registration is met. The ICP point cloud pairing algorithm can obtain the point cloud data offset distances of the two positions.
The lateral offset distance, i.e. the offset distance in the vertical direction of the current positioning position of the traveling vehicle with respect to the reference course, i.e. the distance of the actual position of the vehicle from the reference course, may be expressed as lateral offset with respect to the reference course.
S104, calculating the actual position of the vehicle after calibration according to the transverse offset distance and the current position of the vehicle.
The actual position of the vehicle can be calculated relative to the position offset of the point cloud data acquisition point on the reference line, and the calculated result of the actual position has certain deviation due to the size of the driving vehicle and the combined inertial navigation positioning position, but compared with the positioning position accuracy, the accuracy is greatly improved.
In the embodiment of the invention, the actual offset position of the vehicle is obtained by comparing the point cloud data in the reference route with the point cloud data acquired currently, so that the actual position of the vehicle can be accurately determined, and the influence of the environment on the positioning of the vehicle is reduced.
Optionally, acquiring acquisition position coordinates corresponding to the reference point cloud data; calculating a longitudinal offset of the current position of the vehicle relative to the acquisition position; and calculating the actual position of the vehicle according to the transverse offset distance and the longitudinal offset.
Optionally, driving parameters of the vehicle running to a reference line are calculated according to the actual position of the vehicle, and the vehicle is calibrated according to the driving parameters.
For ease of understanding, a vehicle positioning calibration method according to an embodiment of the present invention is described below with reference to fig. 2 in a practical application scenario according to the embodiment described in fig. 1:
in fig. 2, MN is a vehicle target route, that is, a predetermined driving route of an autonomous vehicle, on which laser radar point cloud data is collected at intervals, and C1 and C2 in fig. 2 are collection points of the point cloud data, respectively.
In fig. 2, point a is the location of the vehicle by combined inertial navigation, usually expressed in terms of longitude and latitude, while point B is assumed to be the actual location of the vehicle.
In the figure, a point C1 is a point cloud data acquisition point closest to a point a, and a lateral offset distance of a point B, that is, an offset distance in the direction of the point a with respect to the point C1 position (target line MN), is obtained by using an ICP point cloud pairing algorithm by comparing the point cloud data of the point a with the point cloud position of the point C1.
And obtaining the actual position of the vehicle on the line PQ according to the transverse offset distance.
Alternatively, the lateral offset distance may also be generally taken as the distance of point B relative to point C1. When the actual position point B of the vehicle is determined, the actual position of the vehicle can be calculated according to the position point A obtained by the current positioning of the vehicle and the transverse offset distance. Specifically, the longitudinal distance of the current positioning position of the vehicle in the direction of the target line MN is taken, that is, the longitudinal coordinates of the point B and the point a are the same. In practice, the vehicle positioning position point a is a longitude and latitude, the collection points C1 and C2 are generally represented as coordinate positions relative to the collection origin, and the actual longitude and latitude coordinates of the point B can be obtained by converting and calculating the longitude and latitude of the point a and the coordinates of the point C1.
Preferably, for a non-linear target line, when calculating the position of the point B, a perpendicular line of the heading angle of the vehicle point a can be made, and since the road width is known and the heading angle of the combined inertial navigation output is obtained, the position of the point B relative to the point C1 can be calculated, the position of the point C1 can be recorded in practice or can be obtained through calculation, and therefore, the actual coordinate position of the point B can be calculated.
In practice, because the distance between C1 and C2 is shorter, the actual position of the vehicle can be approximately obtained by determining the point cloud data acquisition point with the nearest C1, and compared with the direct positioning by combined inertial navigation, the accuracy can be greatly improved.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
Embodiment two:
the foregoing has mainly described a vehicle positioning calibration method, and a vehicle positioning calibration system will be described in detail.
FIG. 3 shows a block diagram of one embodiment of a vehicle positioning calibration system in accordance with an embodiment of the present invention, the system comprising:
acquisition module 310: the method comprises the steps of acquiring the current position of a running vehicle and acquiring laser radar point cloud data of the current position of the running vehicle;
acquisition module 320: the method comprises the steps of acquiring reference point cloud data closest to the current position of a vehicle according to the current position of the vehicle, wherein the reference point cloud data are laser radar point cloud data acquired at intervals of a preset distance when the vehicle runs along a reference route;
optionally, the obtaining module 320 further includes:
and a recording module: the method is used for enabling the vehicle to travel to an initial position, collecting a pair of laser radar point clouds at intervals according to the center line of the current lane, and recording all the collecting positions and the laser radar point cloud data corresponding to the collecting positions.
Optionally, the obtaining module 320 includes:
an acquisition unit: the method comprises the steps of obtaining the position of a point, closest to a reference route, of the current position of the vehicle according to the current position of the vehicle, wherein the point is positioned on the reference route;
and a searching unit: for finding the reference point cloud data closest to the point.
The first calculation module 330: the method comprises the steps of calculating a transverse offset distance of a current position of the vehicle relative to a reference route through an ICP point cloud pairing algorithm according to laser radar point cloud data of the current position of the vehicle and the reference point cloud data;
the second calculation module 340: and the method is used for calculating the actual position of the vehicle after the vehicle is calibrated according to the transverse offset distance and the current position of the vehicle.
Optionally, the second computing module 340 includes:
acquiring the current lane width and the vehicle course angle;
acquiring acquisition position coordinates corresponding to the reference point cloud data;
calculating the longitudinal offset of the vehicle relative to the acquisition position according to the transverse offset distance;
and calculating the actual position of the vehicle according to the transverse offset distance and the longitudinal offset.
Optionally, the second computing module 340 further includes:
and a calibration module: and the driving parameters for the vehicle to travel to a reference line are calculated according to the actual position of the vehicle, and the vehicle is calibrated according to the driving parameters.
The vehicle positioning calibration system can calibrate the obtained position influenced by the environment, ensure the accuracy of vehicle position output and further ensure the driving safety in automatic driving.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the modules, units, and/or method steps of the various embodiments described in connection with the embodiments disclosed herein can be implemented as electronic hardware, or as a combination of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A vehicle positioning calibration method, comprising:
acquiring the current position of a running vehicle, and acquiring laser radar point cloud data of the current position of the running vehicle;
acquiring reference point cloud data closest to the current position of the vehicle according to the current position of the vehicle, wherein the reference point cloud data are laser radar point cloud data acquired at intervals of a preset distance when the vehicle runs along a reference route;
according to the laser radar point cloud data of the current position of the vehicle and the reference point cloud data, calculating the transverse offset distance of the current position of the vehicle relative to a reference route through an ICP point cloud pairing algorithm;
the ICP point cloud pairing algorithm is to combine point cloud data under different coordinates into the same coordinate system, repeatedly select corresponding relation point pairs based on a least square method, and calculate optimal rigid body transformation until convergence accuracy requirements of correct registration are met;
and calculating the actual position of the vehicle after calibration according to the transverse offset distance and the current position of the vehicle.
2. The method of claim 1, wherein the acquiring reference point cloud data closest to the current location of the vehicle based on the current location of the vehicle further comprises, prior to:
and the vehicle runs to an initial position, acquires a pair of laser radar point clouds at intervals according to the central line of the current lane, and records all acquisition positions and laser radar point cloud data corresponding to the acquisition positions.
3. The method according to claim 1 or 2, wherein the acquiring, according to the current position of the vehicle, the reference point cloud data closest to the current position of the vehicle is specifically:
according to the current position of the vehicle, acquiring the position of a point of the current position of the vehicle, which is closest to a reference route, and the point is positioned on the reference route;
and searching the reference point cloud data closest to the point.
4. The method according to claim 1, wherein the calculating the actual position of the vehicle after calibration based on the lateral offset distance and the current position of the vehicle is specifically:
acquiring acquisition position coordinates corresponding to the reference point cloud data;
calculating a longitudinal offset of the current position of the vehicle relative to the acquisition position;
and calculating the actual position of the vehicle according to the transverse offset distance and the longitudinal offset.
5. The method of claim 1 or 4, wherein calculating the actual position of the vehicle after calibration based on the lateral offset distance further comprises:
and calculating driving parameters of the vehicle running to a reference line according to the actual position of the vehicle, and calibrating the vehicle according to the driving parameters.
6. A vehicle positioning calibration system, comprising:
and the acquisition module is used for: the method comprises the steps of acquiring the current position of a running vehicle and acquiring laser radar point cloud data of the current position of the running vehicle;
the acquisition module is used for: the method comprises the steps of acquiring reference point cloud data closest to the current position of a vehicle according to the current position of the vehicle, wherein the reference point cloud data are laser radar point cloud data acquired at intervals of a preset distance when the vehicle runs along a reference route;
a first calculation module: the method comprises the steps of calculating a transverse offset distance of a current position of the vehicle relative to a reference route through an ICP point cloud pairing algorithm according to laser radar point cloud data of the current position of the vehicle and the reference point cloud data;
the ICP point cloud pairing algorithm is to combine point cloud data under different coordinates into the same coordinate system, repeatedly select corresponding relation point pairs based on a least square method, and calculate optimal rigid body transformation until convergence accuracy requirements of correct registration are met;
a second calculation module: and the method is used for calculating the actual position of the vehicle after the vehicle is calibrated according to the transverse offset distance and the current position of the vehicle.
7. The system of claim 6, wherein the acquisition module further comprises:
and a recording module: the method is used for enabling the vehicle to travel to an initial position, collecting a pair of laser radar point clouds at intervals according to the center line of the current lane, and recording all the collecting positions and the laser radar point cloud data corresponding to the collecting positions.
8. The system of claim 6 or 7, wherein the acquisition module comprises:
an acquisition unit: the method comprises the steps of obtaining the position of a point, closest to a reference route, of the current position of the vehicle according to the current position of the vehicle, wherein the point is positioned on the reference route;
and a searching unit: for finding the reference point cloud data closest to the point.
9. The system of claim 6, wherein the second computing module comprises:
acquiring acquisition position coordinates corresponding to the reference point cloud data;
calculating a longitudinal offset of the current position of the vehicle relative to the acquisition position;
and calculating the actual position of the vehicle according to the transverse offset distance and the longitudinal offset.
10. The system of claim 6 or 9, wherein the second computing module further comprises:
and a calibration module: and the driving parameters for the vehicle to travel to a reference line are calculated according to the actual position of the vehicle, and the vehicle is calibrated according to the driving parameters.
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Families Citing this family (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112805534B (en) * | 2019-08-27 | 2024-05-17 | 北京航迹科技有限公司 | System and method for locating a target object |
| CN110542436B (en) * | 2019-09-11 | 2021-10-15 | 百度在线网络技术（北京）有限公司 | Evaluation method, device and equipment of vehicle positioning system and storage medium |
| CN112637779B (en) * | 2019-10-08 | 2023-05-05 | Oppo广东移动通信有限公司 | Method and device for determining moving path, wearable device and storage medium |
| CN112749594B (en) * | 2019-10-31 | 2022-04-22 | 浙江商汤科技开发有限公司 | Information completion method, lane line identification method, intelligent driving method and related products |
| CN111121805A (en) * | 2019-12-11 | 2020-05-08 | 广州赛特智能科技有限公司 | Local positioning correction method, device and medium based on road traffic marking marks |
| CN111127530B (en) * | 2019-12-24 | 2023-06-20 | 山东科技大学 | Multi-period road three-dimensional laser point cloud accurate registration method |
| CN111426326B (en) * | 2020-01-17 | 2022-03-08 | 深圳市镭神智能系统有限公司 | Navigation method, device, equipment, system and storage medium |
| CN111175788B (en) * | 2020-01-20 | 2022-07-08 | 北京主线科技有限公司 | Transverse positioning method and positioning system for automatic driving vehicle |
| CN111812658B (en) * | 2020-07-09 | 2021-11-02 | 北京京东乾石科技有限公司 | Position determination method, device, system and computer readable storage medium |
| CN111857104B (en) * | 2020-08-03 | 2022-05-10 | 广州极飞科技股份有限公司 | Autopilot calibration method and device, electronic equipment and computer-readable storage medium |
| CN112230663B (en) * | 2020-10-28 | 2023-11-10 | 腾讯科技（深圳）有限公司 | Method and device for monitoring vehicle positioning data |
| CN112229396B (en) * | 2020-12-10 | 2021-03-16 | 中智行科技有限公司 | Unmanned vehicle repositioning method, device, equipment and storage medium |
| CN115953905B (en) * | 2023-03-15 | 2023-05-16 | 河北博士林科技开发有限公司 | Vehicle-road cooperative control system based on laser radar |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103257342A (en) * | 2013-01-11 | 2013-08-21 | 大连理工大学 | Joint Calibration Method of 3D Laser Sensor and 2D Laser Sensor |
| CN106541945A (en) * | 2016-11-15 | 2017-03-29 | 广州大学 | A kind of unmanned vehicle automatic parking method based on ICP algorithm |
| CN107133978A (en) * | 2017-05-23 | 2017-09-05 | 哈尔滨工业大学 | Three-dimensional non-rigid point cloud registration method based on consistent point drift algorithm |
| CN108345008A (en) * | 2017-01-23 | 2018-07-31 | 郑州宇通客车股份有限公司 | A kind of target object detecting method, point cloud data extracting method and device |
| CN109100731A (en) * | 2018-07-17 | 2018-12-28 | 重庆大学 | A kind of method for positioning mobile robot based on laser radar scanning matching algorithm |
| CN109186625A (en) * | 2018-10-24 | 2019-01-11 | 北京奥特贝睿科技有限公司 | Intelligent vehicle carries out pinpoint method and system using mixing sampling filter |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103489218B (en) * | 2013-09-17 | 2016-06-29 | 中国科学院深圳先进技术研究院 | Point cloud data quality automatic optimization method and system |
| DE102017111351A1 (en) * | 2017-05-24 | 2018-11-29 | Jena-Optronik Gmbh | Method for detecting and autonomously tracking a target object by means of a LIDAR sensor |
-
2019
- 2019-03-01 CN CN201910154810.5A patent/CN109900298B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103257342A (en) * | 2013-01-11 | 2013-08-21 | 大连理工大学 | Joint Calibration Method of 3D Laser Sensor and 2D Laser Sensor |
| CN106541945A (en) * | 2016-11-15 | 2017-03-29 | 广州大学 | A kind of unmanned vehicle automatic parking method based on ICP algorithm |
| CN108345008A (en) * | 2017-01-23 | 2018-07-31 | 郑州宇通客车股份有限公司 | A kind of target object detecting method, point cloud data extracting method and device |
| CN107133978A (en) * | 2017-05-23 | 2017-09-05 | 哈尔滨工业大学 | Three-dimensional non-rigid point cloud registration method based on consistent point drift algorithm |
| CN109100731A (en) * | 2018-07-17 | 2018-12-28 | 重庆大学 | A kind of method for positioning mobile robot based on laser radar scanning matching algorithm |
| CN109186625A (en) * | 2018-10-24 | 2019-01-11 | 北京奥特贝睿科技有限公司 | Intelligent vehicle carries out pinpoint method and system using mixing sampling filter |
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